Testing for linearity in generalized linear models using kernel smoothing

Chin-Shang Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A test statistic proposed by Li (1999) for testing the adequacy of heteroscedastic nonlinear regression models using nonparametric kernel smoothers is applied to testing for linearity in generalized linear models. Simulation results for models with centered gamma and inverse Gaussian errors are presented to illustrate the performance of the resulting test compared with log-likelihood ratio tests for specific parametric alternatives. The test is applied to a data set of coronary heart disease status (Hosmer and Lemeshow, 1990).

Original languageEnglish (US)
Pages (from-to)339-351
Number of pages13
JournalJournal of Statistical Computation and Simulation
Issue number4
StatePublished - 2001
Externally publishedYes


  • Bandwidth selection
  • Fit comparison test
  • Kernel smoother
  • Quasi-likelihood

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Statistics and Probability


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